DonorsChoose¶

DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.

Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:

How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
How to focus volunteer time on the applications that need the most assistance

The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.

About the DonorsChoose Data Set¶

The train.csv data set provided by DonorsChoose contains the following features:

Feature	Description
`project_id`	A unique identifier for the proposed project. Example: `p036502`
`project_title`	Title of the project. Examples: `Art Will Make You Happy!` `First Grade Fun`
`project_grade_category`	Grade level of students for which the project is targeted. One of the following enumerated values: `Grades PreK-2` `Grades 3-5` `Grades 6-8` `Grades 9-12`
`project_subject_categories`	One or more (comma-separated) subject categories for the project from the following enumerated list of values: `Applied Learning` `Care & Hunger` `Health & Sports` `History & Civics` `Literacy & Language` `Math & Science` `Music & The Arts` `Special Needs` `Warmth` Examples: `Music & The Arts` `Literacy & Language, Math & Science`
`school_state`	State where school is located (Two-letter U.S. postal code). Example: `WY`
`project_subject_subcategories`	One or more (comma-separated) subject subcategories for the project. Examples: `Literacy` `Literature & Writing, Social Sciences`
`project_resource_summary`	An explanation of the resources needed for the project. Example: `My students need hands on literacy materials to manage sensory needs!`
`project_essay_1`	First application essay^*
`project_essay_2`	Second application essay^*
`project_essay_3`	Third application essay^*
`project_essay_4`	Fourth application essay^*
`project_submitted_datetime`	Datetime when project application was submitted. Example: `2016-04-28 12:43:56.245`
`teacher_id`	A unique identifier for the teacher of the proposed project. Example: `bdf8baa8fedef6bfeec7ae4ff1c15c56`
`teacher_prefix`	Teacher's title. One of the following enumerated values: `nan` `Dr.` `Mr.` `Mrs.` `Ms.` `Teacher.`
`teacher_number_of_previously_posted_projects`	Number of project applications previously submitted by the same teacher. Example: `2`

^* See the section Notes on the Essay Data for more details about these features.

Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:

Feature	Description
`id`	A `project_id` value from the `train.csv` file. Example: `p036502`
`description`	Desciption of the resource. Example: `Tenor Saxophone Reeds, Box of 25`
`quantity`	Quantity of the resource required. Example: `3`
`price`	Price of the resource required. Example: `9.95`

Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:

The data set contains the following label (the value you will attempt to predict):

Label	Description
`project_is_approved`	A binary flag indicating whether DonorsChoose approved the project. A value of `0` indicates the project was not approved, and a value of `1` indicates the project was approved.

Notes on the Essay Data¶

__project_essay_1:__ "Introduce us to your classroom"
__project_essay_2:__ "Tell us more about your students"
__project_essay_3:__ "Describe how your students will use the materials you're requesting"
__project_essay_3:__ "Close by sharing why your project will make a difference"

__project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
__project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"

%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer

import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer

from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle

from tqdm import tqdm
import os

from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter

1.1 Reading Data¶

project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')

print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)

Number of data points in train data (109248, 17)
--------------------------------------------------
The attributes of data : ['Unnamed: 0' 'id' 'teacher_id' 'teacher_prefix' 'school_state'
 'project_submitted_datetime' 'project_grade_category'
 'project_subject_categories' 'project_subject_subcategories'
 'project_title' 'project_essay_1' 'project_essay_2' 'project_essay_3'
 'project_essay_4' 'project_resource_summary'
 'teacher_number_of_previously_posted_projects' 'project_is_approved']

print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)

Number of data points in train data (1541272, 4)
['id' 'description' 'quantity' 'price']

1.2 preprocessing of `project_subject_categories`¶

catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_') # we are replacing the & value into 
    cat_list.append(temp.strip())
    
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)

from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
    my_counter.update(word.split())

cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))

1.3 preprocessing of `project_subject_subcategories`¶

sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python

sub_cat_list = []
for i in sub_catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_')
    sub_cat_list.append(temp.strip())

project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)

# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
    my_counter.update(word.split())
    
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))

1.3 Text preprocessing¶

# merge two column text dataframe: 
project_data["essay"] = project_data["project_essay_1"].map(str) +\
                        project_data["project_essay_2"].map(str) + \
                        project_data["project_essay_3"].map(str) + \
                        project_data["project_essay_4"].map(str)

project_data.head(2)

#### 1.4.2.3 Using Pretrained Models: TFIDF weighted W2V

# printing some random reviews
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
print(project_data['essay'].values[99999])
print("="*50)

My students are English learners that are working on English as their second or third languages. We are a melting pot of refugees, immigrants, and native-born Americans bringing the gift of language to our school. \r\n\r\n We have over 24 languages represented in our English Learner program with students at every level of mastery.  We also have over 40 countries represented with the families within our school.  Each student brings a wealth of knowledge and experiences to us that open our eyes to new cultures, beliefs, and respect.\"The limits of your language are the limits of your world.\"-Ludwig Wittgenstein  Our English learner's have a strong support system at home that begs for more resources.  Many times our parents are learning to read and speak English along side of their children.  Sometimes this creates barriers for parents to be able to help their child learn phonetics, letter recognition, and other reading skills.\r\n\r\nBy providing these dvd's and players, students are able to continue their mastery of the English language even if no one at home is able to assist.  All families with students within the Level 1 proficiency status, will be a offered to be a part of this program.  These educational videos will be specially chosen by the English Learner Teacher and will be sent home regularly to watch.  The videos are to help the child develop early reading skills.\r\n\r\nParents that do not have access to a dvd player will have the opportunity to check out a dvd player to use for the year.  The plan is to use these videos and educational dvd's for the years to come for other EL students.\r\nnannan
==================================================
The 51 fifth grade students that will cycle through my classroom this year all love learning, at least most of the time. At our school, 97.3% of the students receive free or reduced price lunch. Of the 560 students, 97.3% are minority students. \r\nThe school has a vibrant community that loves to get together and celebrate. Around Halloween there is a whole school parade to show off the beautiful costumes that students wear. On Cinco de Mayo we put on a big festival with crafts made by the students, dances, and games. At the end of the year the school hosts a carnival to celebrate the hard work put in during the school year, with a dunk tank being the most popular activity.My students will use these five brightly colored Hokki stools in place of regular, stationary, 4-legged chairs. As I will only have a total of ten in the classroom and not enough for each student to have an individual one, they will be used in a variety of ways. During independent reading time they will be used as special chairs students will each use on occasion. I will utilize them in place of chairs at my small group tables during math and reading times. The rest of the day they will be used by the students who need the highest amount of movement in their life in order to stay focused on school.\r\n\r\nWhenever asked what the classroom is missing, my students always say more Hokki Stools. They can't get their fill of the 5 stools we already have. When the students are sitting in group with me on the Hokki Stools, they are always moving, but at the same time doing their work. Anytime the students get to pick where they can sit, the Hokki Stools are the first to be taken. There are always students who head over to the kidney table to get one of the stools who are disappointed as there are not enough of them. \r\n\r\nWe ask a lot of students to sit for 7 hours a day. The Hokki stools will be a compromise that allow my students to do desk work and move at the same time. These stools will help students to meet their 60 minutes a day of movement by allowing them to activate their core muscles for balance while they sit. For many of my students, these chairs will take away the barrier that exists in schools for a child who can't sit still.nannan
==================================================
How do you remember your days of school? Was it in a sterile environment with plain walls, rows of desks, and a teacher in front of the room? A typical day in our room is nothing like that. I work hard to create a warm inviting themed room for my students look forward to coming to each day.\r\n\r\nMy class is made up of 28 wonderfully unique boys and girls of mixed races in Arkansas.\r\nThey attend a Title I school, which means there is a high enough percentage of free and reduced-price lunch to qualify. Our school is an \"open classroom\" concept, which is very unique as there are no walls separating the classrooms. These 9 and 10 year-old students are very eager learners; they are like sponges, absorbing all the information and experiences and keep on wanting more.With these resources such as the comfy red throw pillows and the whimsical nautical hanging decor and the blue fish nets, I will be able to help create the mood in our classroom setting to be one of a themed nautical environment. Creating a classroom environment is very important in the success in each and every child's education. The nautical photo props will be used with each child as they step foot into our classroom for the first time on Meet the Teacher evening. I'll take pictures of each child with them, have them developed, and then hung in our classroom ready for their first day of 4th grade.  This kind gesture will set the tone before even the first day of school! The nautical thank you cards will be used throughout the year by the students as they create thank you cards to their team groups.\r\n\r\nYour generous donations will help me to help make our classroom a fun, inviting, learning environment from day one.\r\n\r\nIt costs lost of money out of my own pocket on resources to get our classroom ready. Please consider helping with this project to make our new school year a very successful one. Thank you!nannan
==================================================
My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations. \r\n\r\nThe materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills. \r\nThey also want to learn through games, my kids don't want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan
==================================================
The mediocre teacher tells. The good teacher explains. The superior teacher demonstrates. The great teacher inspires. -William A. Ward\r\n\r\nMy school has 803 students which is makeup is 97.6% African-American, making up the largest segment of the student body. A typical school in Dallas is made up of 23.2% African-American students. Most of the students are on free or reduced lunch. We aren't receiving doctors, lawyers, or engineers children from rich backgrounds or neighborhoods. As an educator I am inspiring minds of young children and we focus not only on academics but one smart, effective, efficient, and disciplined students with good character.In our classroom we can utilize the Bluetooth for swift transitions during class. I use a speaker which doesn't amplify the sound enough to receive the message. Due to the volume of my speaker my students can't hear videos or books clearly and it isn't making the lessons as meaningful. But with the bluetooth speaker my students will be able to hear and I can stop, pause and replay it at any time.\r\nThe cart will allow me to have more room for storage of things that are needed for the day and has an extra part to it I can use.  The table top chart has all of the letter, words and pictures for students to learn about different letters and it is more accessible.nannan
==================================================

# https://stackoverflow.com/a/47091490/4084039
import re

def decontracted(phrase):
    # specific
    phrase = re.sub(r"won't", "will not", phrase)
    phrase = re.sub(r"can\'t", "can not", phrase)

    # general
    phrase = re.sub(r"n\'t", " not", phrase)
    phrase = re.sub(r"\'re", " are", phrase)
    phrase = re.sub(r"\'s", " is", phrase)
    phrase = re.sub(r"\'d", " would", phrase)
    phrase = re.sub(r"\'ll", " will", phrase)
    phrase = re.sub(r"\'t", " not", phrase)
    phrase = re.sub(r"\'ve", " have", phrase)
    phrase = re.sub(r"\'m", " am", phrase)
    return phrase

sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)

My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations. \r\n\r\nThe materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills. \r\nThey also want to learn through games, my kids do not want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan
==================================================

# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)

My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations.     The materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills.   They also want to learn through games, my kids do not want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan

#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)

My kindergarten students have varied disabilities ranging from speech and language delays cognitive delays gross fine motor delays to autism They are eager beavers and always strive to work their hardest working past their limitations The materials we have are the ones I seek out for my students I teach in a Title I school where most of the students receive free or reduced price lunch Despite their disabilities and limitations my students love coming to school and come eager to learn and explore Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting This is how my kids feel all the time The want to be able to move as they learn or so they say Wobble chairs are the answer and I love then because they develop their core which enhances gross motor and in Turn fine motor skills They also want to learn through games my kids do not want to sit and do worksheets They want to learn to count by jumping and playing Physical engagement is the key to our success The number toss and color and shape mats can make that happen My students will forget they are doing work and just have the fun a 6 year old deserves nannan

# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
            "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
            'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
            's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
            've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
            "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
            "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
            'won', "won't", 'wouldn', "wouldn't"]

# Combining all the above stundents 
from tqdm import tqdm
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    preprocessed_essays.append(sent.lower().strip())

100%|█████████████████████████████████████████████████████████████████████████| 109248/109248 [02:07<00:00, 859.30it/s]

# after preprocesing
preprocessed_essays[20000]

'my kindergarten students varied disabilities ranging speech language delays cognitive delays gross fine motor delays autism they eager beavers always strive work hardest working past limitations the materials ones i seek students i teach title i school students receive free reduced price lunch despite disabilities limitations students love coming school come eager learn explore have ever felt like ants pants needed groove move meeting this kids feel time the want able move learn say wobble chairs answer i love develop core enhances gross motor turn fine motor skills they also want learn games kids not want sit worksheets they want learn count jumping playing physical engagement key success the number toss color shape mats make happen my students forget work fun 6 year old deserves nannan'

1.4 Preprocessing of `project_title`

# similarly you can preprocess the titles also

1.5 Preparing data for models¶

project_data.columns

Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'project_submitted_datetime', 'project_grade_category', 'project_title',
       'project_essay_1', 'project_essay_2', 'project_essay_3',
       'project_essay_4', 'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'project_is_approved',
       'clean_categories', 'clean_subcategories', 'essay'],
      dtype='object')

we are going to consider

   - school_state : categorical data
   - clean_categories : categorical data
   - clean_subcategories : categorical data
   - project_grade_category : categorical data
   - teacher_prefix : categorical data

   - project_title : text data
   - text : text data
   - project_resource_summary: text data (optinal)

   - quantity : numerical (optinal)
   - teacher_number_of_previously_posted_projects : numerical
   - price : numerical

1.5.1 Vectorizing Categorical data¶

https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/

# we use count vectorizer to convert the values into one 
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)
categories_one_hot = vectorizer.fit_transform(project_data['clean_categories'].values)
print(vectorizer.get_feature_names())
print("Shape of matrix after one hot encodig ",categories_one_hot.shape)

['Warmth', 'Care_Hunger', 'History_Civics', 'Music_Arts', 'AppliedLearning', 'SpecialNeeds', 'Health_Sports', 'Math_Science', 'Literacy_Language']
Shape of matrix after one hot encodig  (109248, 9)

# we use count vectorizer to convert the values into one 
vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
sub_categories_one_hot = vectorizer.fit_transform(project_data['clean_subcategories'].values)
print(vectorizer.get_feature_names())
print("Shape of matrix after one hot encodig ",sub_categories_one_hot.shape)

['Economics', 'CommunityService', 'FinancialLiteracy', 'ParentInvolvement', 'Extracurricular', 'Civics_Government', 'ForeignLanguages', 'NutritionEducation', 'Warmth', 'Care_Hunger', 'SocialSciences', 'PerformingArts', 'CharacterEducation', 'TeamSports', 'Other', 'College_CareerPrep', 'Music', 'History_Geography', 'Health_LifeScience', 'EarlyDevelopment', 'ESL', 'Gym_Fitness', 'EnvironmentalScience', 'VisualArts', 'Health_Wellness', 'AppliedSciences', 'SpecialNeeds', 'Literature_Writing', 'Mathematics', 'Literacy']
Shape of matrix after one hot encodig  (109248, 30)

# you can do the similar thing with state, teacher_prefix and project_grade_category also

1.5.2 Vectorizing Text data¶

1.5.2.1 Bag of words¶

# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
text_bow = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_bow.shape)

Shape of matrix after one hot encodig  (109248, 16623)

# you can vectorize the title also 
# before you vectorize the title make sure you preprocess it

1.5.2.2 TFIDF vectorizer¶

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
text_tfidf = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_tfidf.shape)

Shape of matrix after one hot encodig  (109248, 16623)

1.5.2.3 Using Pretrained Models: Avg W2V¶

'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
    print ("Loading Glove Model")
    f = open(gloveFile,'r', encoding="utf8")
    model = {}
    for line in tqdm(f):
        splitLine = line.split()
        word = splitLine[0]
        embedding = np.array([float(val) for val in splitLine[1:]])
        model[word] = embedding
    print ("Done.",len(model)," words loaded!")
    return model
model = loadGloveModel('glove.42B.300d.txt')

# ============================
Output:
    
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495  words loaded!

# ============================

words = []
for i in preproced_texts:
    words.extend(i.split(' '))

for i in preproced_titles:
    words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))

inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
      len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")

words_courpus = {}
words_glove = set(model.keys())
for i in words:
    if i in words_glove:
        words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))


# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/

import pickle
with open('glove_vectors', 'wb') as f:
    pickle.dump(words_courpus, f)


'''

'\n# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039\ndef loadGloveModel(gloveFile):\n    print ("Loading Glove Model")\n    f = open(gloveFile,\'r\', encoding="utf8")\n    model = {}\n    for line in tqdm(f):\n        splitLine = line.split()\n        word = splitLine[0]\n        embedding = np.array([float(val) for val in splitLine[1:]])\n        model[word] = embedding\n    print ("Done.",len(model)," words loaded!")\n    return model\nmodel = loadGloveModel(\'glove.42B.300d.txt\')\n\n# ============================\nOutput:\n    \nLoading Glove Model\n1917495it [06:32, 4879.69it/s]\nDone. 1917495  words loaded!\n\n# ============================\n\nwords = []\nfor i in preproced_texts:\n    words.extend(i.split(\' \'))\n\nfor i in preproced_titles:\n    words.extend(i.split(\' \'))\nprint("all the words in the coupus", len(words))\nwords = set(words)\nprint("the unique words in the coupus", len(words))\n\ninter_words = set(model.keys()).intersection(words)\nprint("The number of words that are present in both glove vectors and our coupus",       len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")\n\nwords_courpus = {}\nwords_glove = set(model.keys())\nfor i in words:\n    if i in words_glove:\n        words_courpus[i] = model[i]\nprint("word 2 vec length", len(words_courpus))\n\n\n# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/\n\nimport pickle\nwith open(\'glove_vectors\', \'wb\') as f:\n    pickle.dump(words_courpus, f)\n\n\n'

# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
with open('glove_vectors', 'rb') as f:
    model = pickle.load(f)
    glove_words =  set(model.keys())

# average Word2Vec
# compute average word2vec for each review.
avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_vectors.append(vector)

print(len(avg_w2v_vectors))
print(len(avg_w2v_vectors[0]))

100%|████████████████████████████████████████████████████████████████████████| 109248/109248 [01:00<00:00, 1806.88it/s]

109248
300

1.5.2.3 Using Pretrained Models: TFIDF weighted W2V¶

# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(preprocessed_essays)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())

# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_vectors.append(vector)

print(len(tfidf_w2v_vectors))
print(len(tfidf_w2v_vectors[0]))

100%|█████████████████████████████████████████████████████████████████████████| 109248/109248 [08:03<00:00, 225.81it/s]

109248
300

# Similarly you can vectorize for title also

1.5.3 Vectorizing Numerical features¶

price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
project_data = pd.merge(project_data, price_data, on='id', how='left')

# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler

# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329.   ... 399.   287.73   5.5 ].
# Reshape your data either using array.reshape(-1, 1)

price_scalar = StandardScaler()
price_scalar.fit(project_data['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")

# Now standardize the data with above maen and variance.
price_standardized = price_scalar.transform(project_data['price'].values.reshape(-1, 1))

price_standardized

array([[0.00098843, 0.00191166, 0.00330448, ..., 0.00153418, 0.00046704,
        0.00070265]])

1.5.4 Merging all the above features¶

we need to merge all the numerical vectors i.e catogorical, text, numerical vectors

print(categories_one_hot.shape)
print(sub_categories_one_hot.shape)
print(text_bow.shape)
print(price_standardized.shape)

(109248, 9)
(109248, 30)
(109248, 16623)
(109248, 1)

# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X = hstack((categories_one_hot, sub_categories_one_hot, text_bow, price_standardized))
X.shape

(109248, 16663)

# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use 
    # a. Title, that describes your plot, this will be very helpful to the reader
    # b. Legends if needed
    # c. X-axis label
    # d. Y-axis label

Assignment 4: Naive Bayes¶

Apply Multinomial NaiveBayes on these feature sets
- Set 1: categorical, numerical features + project_title(BOW) + preprocessed_eassay (BOW)
- Set 2: categorical, numerical features + project_title(TFIDF)+ preprocessed_eassay (TFIDF)

The hyper paramter tuning(find best Alpha)
- Find the best hyper parameter which will give the maximum AUC value
- Consider a wide range of alpha values for hyperparameter tuning, start as low as 0.00001
- Find the best hyper paramter using k-fold cross validation or simple cross validation data
- Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning

Feature importance
- Find the top 10 features of positive class and top 10 features of negative class for both feature sets Set 1 and Set 2 using absolute values of `coef_` parameter of MultinomialNB and print their corresponding feature names

Representation of results
- You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure. Here on X-axis you will have alpha values, since they have a wide range, just to represent those alpha values on the graph, apply log function on those alpha values.
- Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.
- Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points. Please visualize your confusion matrices using seaborn heatmaps.

Conclusion

You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link

2. Naive Bayes

2.1 Splitting data into Train and cross validation(or test): Stratified Sampling

%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer

import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer

from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle

from tqdm import tqdm
import os

from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter

C:\Users\Raman Shinde\Anaconda3\lib\site-packages\gensim\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
  warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")

project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')

project_data.isnull().sum()

Unnamed: 0                                           0
id                                                   0
teacher_id                                           0
teacher_prefix                                       3
school_state                                         0
project_submitted_datetime                           0
project_grade_category                               0
project_subject_categories                           0
project_subject_subcategories                        0
project_title                                        0
project_essay_1                                      0
project_essay_2                                      0
project_essay_3                                 105490
project_essay_4                                 105490
project_resource_summary                             0
teacher_number_of_previously_posted_projects         0
project_is_approved                                  0
dtype: int64

#filling 3 null teacher prefix values with Teacher

project_data["teacher_prefix"].fillna("Teacher",inplace = True)
project_data.isnull().sum()

# merge two column text dataframe: 
project_data["essay"] = project_data["project_essay_1"].map(str) +\
                        project_data["project_essay_2"].map(str) + \
                        project_data["project_essay_3"].map(str) + \
                        project_data["project_essay_4"].map(str)


price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
project_data = pd.merge(project_data, price_data, on='id', how='left')

print(project_data.info())

<class 'pandas.core.frame.DataFrame'>
Int64Index: 109248 entries, 0 to 109247
Data columns (total 20 columns):
Unnamed: 0                                      109248 non-null int64
id                                              109248 non-null object
teacher_id                                      109248 non-null object
teacher_prefix                                  109248 non-null object
school_state                                    109248 non-null object
project_submitted_datetime                      109248 non-null object
project_grade_category                          109248 non-null object
project_subject_categories                      109248 non-null object
project_subject_subcategories                   109248 non-null object
project_title                                   109248 non-null object
project_essay_1                                 109248 non-null object
project_essay_2                                 109248 non-null object
project_essay_3                                 3758 non-null object
project_essay_4                                 3758 non-null object
project_resource_summary                        109248 non-null object
teacher_number_of_previously_posted_projects    109248 non-null int64
project_is_approved                             109248 non-null int64
essay                                           109248 non-null object
price                                           109248 non-null float64
quantity                                        109248 non-null int64
dtypes: float64(1), int64(4), object(15)
memory usage: 17.5+ MB
None

from sklearn.model_selection import train_test_split

#splitting data as 20% to test
y = project_data["project_is_approved"]
X = project_data.drop("project_is_approved",axis = 1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

print(X_train.shape," ",y_train.shape)
print(X_test.shape," ",y_test.shape)

(87398, 19)   (87398,)
(21850, 19)   (21850,)

2.2 Make Data Model Ready: encoding numerical, categorical features

Preprocessing categorical Features

project subject categories

#using code from assignment
# project subject categories
catogories = list(X_train['project_subject_categories'].values)

cat_list = []
for i in catogories:
    temp = ""
    for j in i.split(','): 
        if 'The' in j.split(): 
            j=j.replace('The','') 
        j = j.replace(' ','') 
        temp+=j.strip()+" " 
        temp = temp.replace('&','_') 
    cat_list.append(temp.strip())
    
X_train['clean_categories'] = cat_list
X_train.drop(['project_subject_categories'], axis=1, inplace=True)

from collections import Counter
my_counter = Counter()
for word in X_train['clean_categories'].values:
    my_counter.update(word.split())

cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))


# project subject categories for test data

catogories = list(X_test['project_subject_categories'].values)

cat_list = []
for i in catogories:
    temp = ""
    for j in i.split(','): 
        if 'The' in j.split(): 
            j=j.replace('The','') 
        j = j.replace(' ','') 
        temp+=j.strip()+" " 
        temp = temp.replace('&','_') 
    cat_list.append(temp.strip())
    
X_test['clean_categories'] = cat_list
X_test.drop(['project_subject_categories'], axis=1, inplace=True)

project subject sub_categories

sub_catogories = list(X_train['project_subject_subcategories'].values)
sub_cat_list = []
for i in sub_catogories:
    temp = ""
    for j in i.split(','): 
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') 
        temp +=j.strip()+" "
        temp = temp.replace('&','_')
    sub_cat_list.append(temp.strip())

X_train['clean_subcategories'] = sub_cat_list
X_train.drop(['project_subject_subcategories'], axis=1, inplace=True)

# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in X_train['clean_subcategories'].values:
    my_counter.update(word.split())
    
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))


sub_catogories = list(X_test['project_subject_subcategories'].values)
sub_cat_list = []
for i in sub_catogories:
    temp = ""
    for j in i.split(','): 
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') 
        temp +=j.strip()+" "
        temp = temp.replace('&','_')
    sub_cat_list.append(temp.strip())

X_test['clean_subcategories'] = sub_cat_list
X_test.drop(['project_subject_subcategories'], axis=1, inplace=True)

Teacher Prefix

#preprocessing teacher prefix
prefix = list(X_train['teacher_prefix'].values)
prefix_list = []
for i in prefix:
    temp = ""
    if "." in i: 
            i=i.replace('.','') 
    temp+=i.strip()+" " 
    prefix_list.append(temp.strip())
    
X_train['clean_prefix'] = prefix_list

my_counter = Counter()
for word in X_train['clean_prefix'].values:
  my_counter.update(word.split())

prefix_dict = dict(my_counter)
sorted_prefix_dict = dict(sorted(prefix_dict.items(), key=lambda kv: kv[1]))
print(sorted_prefix_dict)


#preprocessing teacher prefix for test data
prefix = list(X_test['teacher_prefix'].values)
prefix_list = []
for i in prefix:
    temp = ""
    if "." in i: 
            i=i.replace('.','') 
    temp+=i.strip()+" " 
    prefix_list.append(temp.strip())
    
X_test['clean_prefix'] = prefix_list

{'Dr': 11, 'Teacher': 1900, 'Mr': 8519, 'Ms': 31168, 'Mrs': 45800}

Project Grade Category

# preprocessing of grade category for train data

grade = list(X_train['project_grade_category'].values)
grade_list = []
for i in grade:
    temp = ""
    if "Grades" in i:
      i = i.replace("Grades","")
    if "6-8" in i:
      i = i.replace("6-8","six_eight")
    if "3-5" in i:
      i = i.replace("3-5","three_five")
    if "9-12" in i:
      i = i.replace("9-12","nine_twelve")
    if "PreK-2" in i:
      i = i.replace("PreK-2","prek_two")
    temp+=i.strip()+" " 
    grade_list.append(temp.strip())
    
X_train['clean_grade'] = grade_list

my_counter = Counter()
for word in X_train['clean_grade'].values:
  my_counter.update(word.split())

grade_dict = dict(my_counter)
sorted_grade_dict = dict(sorted(grade_dict.items(), key=lambda kv: kv[1]))
print(sorted_grade_dict)

# preprocessing of grade category for test data

grade = list(X_test['project_grade_category'].values)
grade_list = []
for i in grade:
    temp = ""
    if "Grades" in i:
      i = i.replace("Grades","")
    if "6-8" in i:
      i = i.replace("6-8","six_eight")
    if "3-5" in i:
      i = i.replace("3-5","three_five")
    if "9-12" in i:
      i = i.replace("9-12","nine_twelve")
    if "PreK-2" in i:
      i = i.replace("PreK-2","prek_two")
    temp+=i.strip()+" " 
    grade_list.append(temp.strip())
    
X_test['clean_grade'] = grade_list

{'nine_twelve': 8709, 'six_eight': 13487, 'three_five': 29679, 'prek_two': 35523}

School State

#no need of preprocessing on school state

state = X_train["school_state"].value_counts()
sorted_state = dict(state)
sorted_state_dict = dict(sorted(sorted_state.items(), key=lambda kv: kv[1]))
X_train["clean_state"] = X_train["school_state"]

#similarly for X_test
X_test["clean_state"] = X_test["school_state"]

Preprocessing Numerical Feature

Standardizing Price

#Normalizing data rather than standardizing to avoid -ve values

from sklearn.preprocessing import MinMaxScaler
price_scalar = MinMaxScaler()
price_scalar.fit(project_data['price'].values.reshape(-1, 1)) 
#print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")

#train data price standardization
price_normalised = price_scalar.transform(X_train['price'].values.reshape(-1, 1))

#test data price stanardization. Fit method applied on X_train
test_price_normalised = price_scalar.transform(X_test['price'].values.reshape(-1, 1))

Standardizing Quantity

price_scalar = MinMaxScaler()
price_scalar.fit(X_train["quantity"].values.reshape(-1, 1))
#print(f"Mean of Quantity : {price_scalar.mean_[0]}, Standard deviation of Quantity : {np.sqrt(price_scalar.var_[0])}")

#train data quantity standardization
quantity_normalised = price_scalar.transform(X_train["quantity"].values.reshape(-1, 1))

#test data quantity stanardization. Fit method applied on X_train
test_quantity_normalised = price_scalar.transform(X_test["quantity"].values.reshape(-1, 1))

C:\Users\Raman Shinde\Anaconda3\lib\site-packages\sklearn\utils\validation.py:595: DataConversionWarning:

Data with input dtype int64 was converted to float64 by MinMaxScaler.

Standardizing number ppp

price_scalar = MinMaxScaler()
price_scalar.fit(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) 
#print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")

#train data ppp standardization
number_ppp_normalised = price_scalar.transform(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))

#test data price stanardization. Fit method applied on X_train
test_number_ppp_normalised = price_scalar.transform(X_test['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))

C:\Users\Raman Shinde\Anaconda3\lib\site-packages\sklearn\utils\validation.py:595: DataConversionWarning:

Data with input dtype int64 was converted to float64 by MinMaxScaler.

Vectorizing of Categorical data

Vectorizing project categories and subcategories

vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)

# fitting on train data
vectorizer.fit(X_train['clean_categories'].values)
print(vectorizer.get_feature_names())
categories_feature = vectorizer.get_feature_names()

# for train data
categories_one_hot = vectorizer.transform(X_train['clean_categories'].values)

print("Shape of matrix after one hot encodig ",categories_one_hot.shape)

# for test data
test_categories_one_hot = vectorizer.transform(X_test['clean_categories'].values)

['Warmth', 'Care_Hunger', 'History_Civics', 'Music_Arts', 'AppliedLearning', 'SpecialNeeds', 'Health_Sports', 'Math_Science', 'Literacy_Language']
Shape of matrix after one hot encodig  (87398, 9)

Vectorizing project subcategories

vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)

# fitting on train data
vectorizer.fit(X_train['clean_subcategories'].values)
print(vectorizer.get_feature_names())
subcategories_feature = vectorizer.get_feature_names()

# for train data
sub_categories_one_hot = vectorizer.transform(X_train['clean_subcategories'].values)
print("Shape of matrix after one hot encodig ",sub_categories_one_hot.shape)

# for test data
test_sub_categories_one_hot = vectorizer.transform(X_test['clean_subcategories'].values)

['Economics', 'CommunityService', 'FinancialLiteracy', 'ParentInvolvement', 'Extracurricular', 'Civics_Government', 'ForeignLanguages', 'NutritionEducation', 'Warmth', 'Care_Hunger', 'SocialSciences', 'PerformingArts', 'CharacterEducation', 'TeamSports', 'Other', 'College_CareerPrep', 'History_Geography', 'Music', 'Health_LifeScience', 'EarlyDevelopment', 'ESL', 'Gym_Fitness', 'EnvironmentalScience', 'VisualArts', 'Health_Wellness', 'AppliedSciences', 'SpecialNeeds', 'Literature_Writing', 'Mathematics', 'Literacy']
Shape of matrix after one hot encodig  (87398, 30)

vectorizing teacher prefix

vectorizer = CountVectorizer(vocabulary=list(prefix_dict.keys()), lowercase=False, binary=True)

# fitting on train data
vectorizer.fit(X_train['clean_prefix'].values)
print(vectorizer.get_feature_names())
prefix_feature = vectorizer.get_feature_names()

# for train data
prefix_one_hot = vectorizer.transform(X_train['clean_prefix'].values)
print("Shape of matrix after one hot encodig ",prefix_one_hot.shape)

# for test data
test_prefix_one_hot = vectorizer.transform(X_test['clean_prefix'].values)

['Mrs', 'Ms', 'Teacher', 'Mr', 'Dr']
Shape of matrix after one hot encodig  (87398, 5)

Vectorizing school state and grade

vectorizer = CountVectorizer(vocabulary=list(grade_dict.keys()), lowercase=False, binary=True)

# fitting on train data
vectorizer.fit(X_train['clean_grade'].values)
print(vectorizer.get_feature_names())
grade_feature = vectorizer.get_feature_names()

# for train data
grade_one_hot = vectorizer.transform(X_train['clean_grade'].values)
print("Shape of matrix after one hot encodig ",grade_one_hot.shape)

# for test data
test_grade_one_hot = vectorizer.transform(X_test['clean_grade'].values)

vectorizer = CountVectorizer(vocabulary=list(sorted_state_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(X_train['clean_state'].values)

state_feature = vectorizer.get_feature_names()
print(vectorizer.get_feature_names())


state_one_hot = vectorizer.transform(X_train['clean_state'].values)
test_state_one_hot = vectorizer.transform(X_test['clean_state'].values)

['prek_two', 'three_five', 'six_eight', 'nine_twelve']
Shape of matrix after one hot encodig  (87398, 4)
['VT', 'WY', 'ND', 'MT', 'RI', 'NE', 'SD', 'AK', 'DE', 'NH', 'WV', 'ME', 'DC', 'HI', 'NM', 'KS', 'ID', 'IA', 'AR', 'CO', 'MN', 'OR', 'KY', 'MS', 'NV', 'MD', 'CT', 'TN', 'UT', 'AL', 'WI', 'VA', 'AZ', 'NJ', 'OK', 'WA', 'LA', 'MA', 'OH', 'MO', 'IN', 'PA', 'MI', 'GA', 'SC', 'IL', 'NC', 'FL', 'NY', 'TX', 'CA']

print(len(categories_feature),categories_one_hot.shape)
print(len(subcategories_feature),sub_categories_one_hot.shape)
print(len(grade_feature),grade_one_hot.shape)
print(len(prefix_feature),prefix_one_hot.shape)
print(len(state_feature),state_one_hot.shape)

9 (87398, 9)
30 (87398, 30)
4 (87398, 4)
5 (87398, 5)
51 (87398, 51)

2.3 Make Data Model Ready: encoding eassay, and project_title

Preprocessing of Text Feature for both teat and train data

#using function and stopwords form assignemnt

import re

def decontracted(phrase):
    # specific
    phrase = re.sub(r"won't", "will not", phrase)
    phrase = re.sub(r"can\'t", "can not", phrase)

    # general
    phrase = re.sub(r"n\'t", " not", phrase)
    phrase = re.sub(r"\'re", " are", phrase)
    phrase = re.sub(r"\'s", " is", phrase)
    phrase = re.sub(r"\'d", " would", phrase)
    phrase = re.sub(r"\'ll", " will", phrase)
    phrase = re.sub(r"\'t", " not", phrase)
    phrase = re.sub(r"\'ve", " have", phrase)
    phrase = re.sub(r"\'m", " am", phrase)
    return phrase
  
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
            "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
            'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
            's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
            've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
            "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
            "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
            'won', "won't", 'wouldn', "wouldn't"]

preprocessing of project essay

from tqdm import tqdm

#for train data
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(X_train['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    preprocessed_essays.append(sent.lower().strip())
    
test_preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(X_test['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    test_preprocessed_essays.append(sent.lower().strip())

100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 87398/87398 [01:29<00:00, 978.58it/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21850/21850 [00:21<00:00, 1038.79it/s]

Preprocessing of project title

preprocessed_title = []
# tqdm is for printing the status bar
for sentance in tqdm(X_train['project_title'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    preprocessed_title.append(sent.lower().strip())
    
# for test data
test_preprocessed_title = []
# tqdm is for printing the status bar
for sentance in tqdm(X_test['project_title'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    test_preprocessed_title.append(sent.lower().strip())

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 87398/87398 [00:04<00:00, 21611.02it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21850/21850 [00:01<00:00, 20426.21it/s]

Vectorizing Text Feature

BOW

vectorizer = CountVectorizer(min_df=10,ngram_range=(2,2),max_features=5000)
#fit using train data
vectorizer.fit(preprocessed_essays)

essay_feature = vectorizer.get_feature_names()

# for train data
text_bow = vectorizer.transform(preprocessed_essays)
print("Shape of train matrix : ",text_bow.shape)
# for test data
test_text_bow = vectorizer.transform(test_preprocessed_essays)
print("Shape of test matrix : ",test_text_bow.shape)


# for title
vectorizer.fit(preprocessed_title)
title_feature = vectorizer.get_feature_names()
# for train data
title_bow = vectorizer.transform(preprocessed_title)
print("Shape of train matrix : ",title_bow.shape)
# for test data
test_title_bow = vectorizer.transform(test_preprocessed_title)
print("Shape of test matrix : ",test_title_bow.shape)

Shape of train matrix :  (87398, 5000)
Shape of test matrix :  (21850, 5000)
Shape of train matrix :  (87398, 3305)
Shape of test matrix :  (21850, 3305)

print(len(essay_feature))
print(len(title_feature))

5000
3305

TFIDF

vectorizer = TfidfVectorizer(min_df=10,ngram_range=(2,2),max_features=5000)
#fit using train data
vectorizer.fit(preprocessed_essays)

# storing features in a list
essay_feature_tfidf = vectorizer.get_feature_names()

# for train data
text_tfidf = vectorizer.transform(preprocessed_essays)
print("Shape of train matrix : ",text_tfidf.shape)
# for test data
test_text_tfidf = vectorizer.transform(test_preprocessed_essays)
print("Shape of test matrix : ",test_text_tfidf.shape)


# for title
vectorizer.fit(preprocessed_title)
title_feature_tfidf = vectorizer.get_feature_names()


# for train data
title_tfidf = vectorizer.transform(preprocessed_title)
print("Shape of train matrix : ",title_tfidf.shape)
# for test data
test_title_tfidf = vectorizer.transform(test_preprocessed_title)
print("Shape of test matrix : ",test_title_tfidf.shape)

Shape of train matrix :  (87398, 5000)
Shape of test matrix :  (21850, 5000)
Shape of train matrix :  (87398, 3305)
Shape of test matrix :  (21850, 3305)

2.4 Appling NB() on different kind of featurization as mentioned in the instructions

Apply Naive Bayes on different kind of featurization as mentioned in the instructions
For Every model that you work on make sure you do the step 2 and step 3 of instrucations

SET

#combining all feature into one
from scipy.sparse import hstack

set1 = hstack((categories_one_hot,sub_categories_one_hot,prefix_one_hot,grade_one_hot,state_one_hot,text_bow,title_bow,price_normalised,quantity_normalised,number_ppp_normalised))
set1_t = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_prefix_one_hot,test_grade_one_hot,test_state_one_hot,test_text_bow,test_title_bow,test_price_normalised,test_quantity_normalised,test_number_ppp_normalised))



set2 = hstack((categories_one_hot,sub_categories_one_hot,prefix_one_hot,state_one_hot,grade_one_hot,text_tfidf,title_tfidf,price_normalised,quantity_normalised,number_ppp_normalised))
set2_t = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_prefix_one_hot,test_state_one_hot,test_grade_one_hot,test_text_tfidf,test_title_tfidf,test_price_normalised,test_quantity_normalised,test_number_ppp_normalised))

# storing all features names in one list
set1_feature = categories_feature + subcategories_feature + prefix_feature + grade_feature + state_feature + essay_feature + title_feature 
set2_feature = categories_feature + subcategories_feature + prefix_feature + grade_feature + state_feature + essay_feature_tfidf + title_feature_tfidf

set1_feature.append("price")
set1_feature.append("quantity")
set1_feature.append("number")

set2_feature.append("price")
set2_feature.append("quantity")
set2_feature.append("number")

print(set1.shape)
print(set1_t.shape)
print(set2.shape)
print(set2_t.shape)

print(len(set1_feature))
print(len(set2_feature))

(87398, 8407)
(21850, 8407)
(87398, 8407)
(21850, 8407)
8407
8407

from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve

# using gridsearch cv to find the best hyperparameter
parameter = dict(alpha=[0.0001,0.0001,0.001,0.1,1,10,100,1000,10000])
alpha=[0.0001,0.0001,0.001,0.1,1,10,100,1000,10000]

2.4.1 Applying Naive Bayes on BOW, SET 1¶

mnb = MultinomialNB()
grid = GridSearchCV(mnb,parameter,scoring="roc_auc",n_jobs=-1,cv=10)

grid.fit(set1,y_train)

GridSearchCV(cv=10, error_score='raise-deprecating',
       estimator=MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True),
       fit_params=None, iid='warn', n_jobs=-1,
       param_grid={'alpha': [0.0001, 0.0001, 0.001, 0.1, 1, 10, 100, 1000, 10000]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring='roc_auc', verbose=0)

print(grid.best_estimator_)
print(grid.best_index_)
print("Best Parameter Found:- ",grid.best_params_)
print(grid.best_score_)

MultinomialNB(alpha=1, class_prior=None, fit_prior=True)
4
Best Parameter Found:-  {'alpha': 1}
0.673716045718096

#converting results to dataframe
df = pd.DataFrame(data = grid.cv_results_)

# getting into list
train_score = []
test_score = []

for i in range(len(df)):
  test_score.append(df.iloc[i]["mean_test_score"])
  train_score.append(df.iloc[i]["mean_train_score"])
    
print(train_score)
print(test_score )

[0.742180442461038, 0.742180442461038, 0.741612287490637, 0.7370948757060873, 0.7298128590656061, 0.7109621505065127, 0.6153810700516493, 0.5613378368459767, 0.5512697918873224]
[0.6629700447235789, 0.6629700447235789, 0.6649240352601714, 0.6715703459890604, 0.673716045718096, 0.6670338855771905, 0.5997712839719447, 0.558568650624583, 0.5504141257448559]

plt.plot(alpha,train_score)
plt.plot(alpha,test_score)
plt.legend(["train","test"])
plt.xscale("log")
plt.xlabel("Alpha")
plt.ylabel("Accuracy Score")
plt.title("train/test AUC score")
plt.show()

# using multinomial NB for feature predictions
mnb = MultinomialNB(alpha=1)
mnb.fit(set1,y_train)

MultinomialNB(alpha=1, class_prior=None, fit_prior=True)

y1_predict = mnb.predict(set1)
cm1 = confusion_matrix(y_train,y1_predict)

y1_predict_t = mnb.predict(set1_t)
cm1_t = confusion_matrix(y_test,y1_predict_t)
# https://seaborn.pydata.org/generated/seaborn.heatmap.html
plt.figure(6)
plt.subplot(121)
plt.title("Train data heatmap")
sns.heatmap(cm1, annot=True, fmt="d",)
plt.subplot(122)
plt.title("Test heatmap")
sns.heatmap(cm1_t, annot=True, fmt="d",)

<matplotlib.axes._subplots.AxesSubplot at 0x1de8f0b780>

AUC Plotting

# probabilities calcultion
y1_predict_prob = mnb.predict_proba(set1_t)[:,1]
y1_predict_prob_train = mnb.predict_proba(set1)[:,1]

# took referance from https://stackoverflow.com/questions/25009284/how-to-plot-roc-curve-in-python
#fpr,tpr 
fpr,tpr,thre = roc_curve(y_test,y1_predict_prob)

# am i doing it right here......?
fpr_train,tpr_train,thre_train = roc_curve(y_train,y1_predict_prob_train)

# auc calculation for test data
roc_auc1 = metrics.auc(fpr,tpr)

# auc calculation for train data
roc_auc_train1 = metrics.auc(fpr_train,tpr_train)

# took referance from https://stackoverflow.com/questions/25009284/how-to-plot-roc-curve-in-python
plt.plot(fpr,tpr,"b--",label = 'AUC test = %0.2f'%roc_auc1)
plt.plot(fpr_train,tpr_train,"y--",label = 'AUC train = %0.2f'%roc_auc_train1)
plt.title("AUC plot")
plt.xlabel("False positive rate")
plt.ylabel("True positive rate")
plt.plot([0, 1], [0, 1],'r--',label = "AUC ideal = 0.5")
plt.xlim([0,1])
plt.ylim([0,1])
plt.legend(loc = "upper left")
plt.show()

2.4.1.1 Top 10 important features of positive class from SET 1¶

# top 10 +ve features
# taking into account feature_log_probabilities and coef_

top_features = pd.DataFrame(data = mnb.feature_log_prob_[1],columns=["Feature log probab"])
top_features["Feature coefficients"] = mnb.coef_.T

# sortind by Feature coefficients
top_positive = top_features.sort_values(by =["Feature coefficients"],ascending=False)

print("Top 10 important features for +ve class using BOW:-")

top_positive["Feature Names"] = [set1_feature[x] for x in top_positive.index.values]

print(top_positive[0:10])

Top 10 important features for +ve class using BOW:-
      Feature log probab  Feature coefficients      Feature Names
2664           -3.823796             -3.823796        my students
39             -4.618265             -4.618265                Mrs
8              -4.697413             -4.697413  Literacy_Language
44             -4.880226             -4.880226           prek_two
7              -4.960791             -4.960791       Math_Science
40             -5.017373             -5.017373                 Ms
45             -5.054518             -5.054518         three_five
38             -5.128325             -5.128325           Literacy
2503           -5.190866             -5.190866      many students
3841           -5.275434             -5.275434      students come

2.4.1.2 Top 10 important features of negative class from SET 1¶

# top 10 -ve features
# taking into account feature_log_probabilities and coef_

top_features_neg = pd.DataFrame(data = mnb.feature_log_prob_[0],columns=["Feature log probab"])
top_features_neg["Feature coefficients"] = mnb.coef_.T

# sortind by Feature coefficients
top_negative = top_features_neg.sort_values(by =["Feature log probab"],ascending=False)

print("Top 10 important features for +ve class using BOW:-")

top_negative["Feature Names"] = [set1_feature[x] for x in top_negative.index.values]

print(top_negative[0:10])

Top 10 important features for +ve class using BOW:-
      Feature log probab  Feature coefficients      Feature Names
2664           -3.848514             -3.823796        my students
39             -4.632527             -4.618265                Mrs
8              -4.792323             -4.697413  Literacy_Language
44             -4.841122             -4.880226           prek_two
7              -4.859959             -4.960791       Math_Science
40             -4.936806             -5.017373                 Ms
45             -5.052025             -5.054518         three_five
3841           -5.242584             -5.275434      students come
2503           -5.250701             -5.190866      many students
37             -5.280656             -5.345032        Mathematics

2.4.2 Applying Naive Bayes on TFIDF, SET 2¶

grid.fit(set2,y_train)

GridSearchCV(cv=10, error_score='raise-deprecating',
       estimator=MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True),
       fit_params=None, iid='warn', n_jobs=-1,
       param_grid={'alpha': [0.0001, 0.0001, 0.001, 0.1, 1, 10, 100, 1000, 10000]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring='roc_auc', verbose=0)

print(grid.best_estimator_)
print(grid.best_index_)
print(grid.best_params_)
print(grid.best_score_)

MultinomialNB(alpha=1, class_prior=None, fit_prior=True)
4
{'alpha': 1}
0.6466219710091533

#converting results to dataframe
df = pd.DataFrame(data = grid.cv_results_)

# getting into list
train_score = []
test_score = []

for i in range(len(df)):
  test_score.append(df.iloc[i]["mean_test_score"])
  train_score.append(df.iloc[i]["mean_train_score"])
    
print(train_score)
print(test_score )

[0.7369822671480691, 0.7369822671480691, 0.7369768198284575, 0.7359701351563632, 0.7222205645930352, 0.6326731279337701, 0.5471418596223373, 0.5267899896779262, 0.5283460376446412]
[0.638503222511107, 0.638503222511107, 0.6386265452582562, 0.6423305385603569, 0.6466219710091533, 0.5962455886897798, 0.5405792336289991, 0.5257528652798754, 0.528131242084062]

AUC plotting

plt.plot(alpha,train_score)
plt.plot(alpha,test_score)
plt.legend(["train","test"])
plt.xscale("log")
plt.xlabel("Alpha")
plt.ylabel("Accuracy Score")
plt.title("train/test AUC plot")
plt.show()

# using multinomial NB for feature predictions
mnb = MultinomialNB(alpha=1)
mnb.fit(set2,y_train)

MultinomialNB(alpha=1, class_prior=None, fit_prior=True)

y2_predict = mnb.predict(set2)
cm2 = confusion_matrix(y_train,y2_predict)

y2_predict_t = mnb.predict(set2_t)
cm2_t = confusion_matrix(y_test,y2_predict_t)


# https://seaborn.pydata.org/generated/seaborn.heatmap.html
plt.figure(6)

plt.subplot(121)
plt.title("Train data heatmap")
sns.heatmap(cm2, annot=True, fmt="d",)

plt.subplot(122)
plt.title("Test heatmap")
sns.heatmap(cm2_t, annot=True, fmt="d",)

<matplotlib.axes._subplots.AxesSubplot at 0x1dec17c7b8>